PEACE [1] is a planner–executor agent for a single-robot systems: a large language model (LLM) turns a natural-language mission into one typed plan of tool calls, an executor dispatches it deterministically over ROS 2, and an independent constraint layer validates every motion command. Many missions that motivate language-tasked drones, such as area survey, search, or inspection of distributed infrastructure, are naturally multi-robot. This project extends the planner–executor paradigm from single-robot system to a multi-robot system: the planner must decompose a mission into per-robot plans with dependencies, the executor must coordinate several robots, the world model must be shared, and the constraint layer must enforce inter-robot constraints such as separation and operational environment partitioning in addition to per-robot geofences.
Large language models (LLMs) are increasingly used as high-level planners for robots: a natural-language mission is turned into a sequence of actions that a classical controller executes. Recent work has shown that such LLM-controlled robots can be jailbroken into performing physically harmful actions, often with 100% attack success, on commercial platforms [1]. Proposed defenses either wrap the planner in a guardrail [2] or restrict what the planner may emit. Planner–executor agents such as PEACE [3] take the second route: the LLM produces a single, fully typed plan over a fixed tool schema instead of free-form code or a step-by-step reasoning loop. Whether this architectural choice actually reduces the attack surface has never been measured. This project builds a reusable red-teaming harness for LLM-enabled robot planners in simulation and uses it to compare architectures under a common set of attacks.